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A new method for analysing discrete life history data with missing covariate values

Authors


Address for correspondence: E. A. Catchpole, School of Physical, Environmental and Mathematical Sciences, University of New South Wales at the Australian Defence Force Academy, Canberra, ACT 2600, Australia.
E-mail: e.catchpole@adfa.edu.au

Abstract

Summary.  Regular censusing of wild animal populations produces data for estimating their annual survival. However, there can be missing covariate data; for instance time varying covariates that are measured on individual animals often contain missing values. By considering the transitions that occur from each occasion to the next, we derive a novel expression for the likelihood for mark–recapture–recovery data, which is equivalent to the traditional likelihood in the case where no covariate data are missing, and which provides a natural way of dealing with covariate data that are missing, for whatever reason. Unlike complete-case analysis, this approach does not exclude incompletely observed life histories, uses all available data and produces consistent estimators. In a simulation study it performs better overall than alternative methods when there are missing covariate data.

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